Are High-Income Areas More Sensitive to Crime than Low-Income Areas?

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A Spatial Analysis by Jacki Murdock

In the midterm we discovered that transit use in low income areas is not

significantly impacted by crime.

However, this may be because the site selected was transit dependent,

and therefore was not as sensitive to crime because they do not have

access to another mode of travel.

So, is transit use more affected by crime (i.e. more sensitive to crime) in

higher income areas compared with lower income areas?

I chose two sites based on the Los Angeles Times “mapping LA” site’s

median income data: Hancock Park and Watts.

Data: METRO bus ridership data for every month (September 2011-January 2012) LAPD crime data for

those months LA Times Mapping LA, ESRI, 2010 TIGER, MIT GIS Services

The Question

Hancock Park Watts

• 17,346 people per square mile

• 62% Latino

• 37% Black

• $25,161 Median Income

• 2.9% have a four year degree

• 48 Crimes in October, 2011

• 76 Bus Stops

• 6,459 people per square mile

•71% White

•13% Asian

• $85,277 Median Income

• 56.2% have a four year degree

• 25 Crimes in October, 2011

• 55 Bus Stops

Process of Site Selection

In order to determine if higher income, less transit dependent communities were more sensitive to crime than lower income, transit dependent communities, I chose a higher and lower income area based on the LATimes Crime Mapping rankings of neighborhoods.

Hancock Park

Watts

Bus Stop

0 0.5 10.25 Miles

Bus Stop

0 0.5 10.25 Miles

105 FreewayWilshire Boulevard

Total Ridership and Crime: W

atts

Tota

l Rid

ersh

ip a

nd C

rime:

Han

cock

Par

k

0 0.5 10.25 Miles

105 FreewayWilshire Boulevard

Mean Center: W

attsM

ean

Cent

er: H

anco

ckThe Mean Center represents

the shortest average distance from crime or ridership. In Watts, the

average distance between crime and average distance

between bus riders is shorter than in Hancock Park .

0 0.5 10.25 Miles

105 FreewayWilshire Boulevard

Standard Distance: Watts

Stan

dard

Dist

ance

: Han

cock

This circle represents one standard deviation from the mean. 66% of crime

and ridership happen within these circles. So, the smaller the circle, the more

clustered the crime/ridership. Much of the ridership in Watts overlaps with most

of the crime incidents, whereas most of the crime/ridership does not intersect in

Hancock Park

0 0.5 10.25 Miles

105 FreewayWilshire Boulevard

Standard Deviation Ellipse: Watts

Stan

dard

Dev

iatio

n El

lipse

: Han

cock

The standard distance assumes a normal distribution of features within the circle. However, this is very rarely

the case. This measure shows the direction and the distribution of crime and ridership. In Hancock park, riders

are much less distributed than in Watts. Yet Crime seems to be similarly

distributed.

0 0.5 10.25 Miles

Ridership Crime0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Clustered or Dispersed: Moran's Index

WattsHancock Park

Next, I ran a spatial autocorrelation test to see if the general pattern of features is clustered or dispersed.

In the Moran’s Index, the higher the value, the more clustered.Crime and Ridership are clustered in both Watts and Hancock Park

*All of the variables had statistically significant results at P-values below 0.05.

Wilshire Boulevard

Crime Incidents Hot Spot: Hancock Park

Wilshire Boulevard

Tran

sit R

ider

ship

Hot

Spo

t: Ha

ncoc

k Pa

rk

Wilshire Boulevard

To know more about why crime and

ridership are clustered, I performed

a Hot Spot Analysis

0 0.5 10.25 Miles

Crime Incidents Hot Spot: W

atts105 Freeway105 Freeway

Tran

sit R

ider

ship

Hot

Spo

t: W

atts

The Hot Spots, in red, are surrounded around high levels of ridership/crime. In addition, they

are surrounded around more ridership/crime than its neighbors.

This may be way Watts has no significant hot spots, because high

crime levels occur more dispersedly.

0 0.5 10.25 Miles

105 FreewayWilshire Boulevard

Average Distance to Crime and Ridership: W

atts

Aver

age

Dist

ance

to C

rime

and

Ride

rshi

p: H

anco

ck P

ark

Next, I used Network Analyst to find the average distance to crime

for each transit stop. Then, I ran an OLS Regression to find out

where stops proximity to crime was effecting ridership.

105 FreewayWilshire Boulevard

Average Distance to Crime and Ridership: W

atts

Aver

age

Dist

ance

to C

rime

and

Ride

rshi

p: H

anco

ck P

ark

Next, I used Network Analyst to find the average distance to crime

for each transit stop. Then, I ran an OLS Regression to find out

where stops proximity to crime was effecting ridership.

Watts Hancock Park0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

R-Squared

R-Squared

R-Squared tells us how well the regression was able to predict crimes’ effect on transit use. In our case, the R-squared is extremely low in both cases, but is slightly higher in Watts, possibly due to its higher incidents of crime.

*All of the variables had statistically significant results at P-values below 0.05.

Implications

• We have seen that there are significant differences in sensitivity

to crime between a high-income and low-income area.

• Watts is not as sensitive to crime as is Hancock Park

• Presumably, this is because those transit stops where most

crime occurs in Hancock Park are not used as often because

their residents have access to vehicles, while many Watts’

residents do not.

Inset Map Graduated Symbols Aggregating Attribute Fields Attribute sub-set selections Geographic sub-set selections Geoprocessing Geocoding Charts Network Analyst (to find average distance to crime for each

transit stop) 5 Models Spatial Statistics (Mean Center, Standard Distance, Standard

Deviation Ellipse, Getis-Ord General Gi, Spatial Autocorrelation, OLS Regression)

Skills Used